Can Desktop PC · 2× GeForce RTX 3090 (48 GB) · 128 GB RAM run LFM2.5 24B-A2B?

48 GB VRAM + 128 GB RAM · 936 GB/s memory bandwidth · LFM2.5 24B-A2B has 24B parameters (2B active per token).

Short answer

Yes, excellent for chat and comfortable or better for every workload we test.

Best quantization for chat: Q4_K_M (14 GB of weights) on Ollama. Generation: 270–450 tok/s, leaving about 32 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

Workload	Verdict	Quant	Generation	First reply	Context
Chatwith Open WebUI	Excellent	Q4_K_M	270–450 tok/s	under 1 s	8K	Details
Coding questionswith Continue	Excellent	Q8_0	150–260 tok/s	under 1 s	16K	Details
Coding in a repositorywith Aider	Comfortable	Q8_0	140–230 tok/s	≈2 s	32K	Details
Agentic codingwith OpenCode	Comfortable	Q8_0	130–220 tok/s	≈2 s	32K	Details
Long documents (64K)with Open WebUI	Excellent	Q8_0	110–180 tok/s	≈5 s	64K	Details

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

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Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.

Check my exact setup